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Modeling the multiwavelength spectral energy distributions (SEDs) of blazarsprovides key insights into the underlying physical processes responsible forthe emission. While SED modeling with self-consistent models is computationallydemanding, it is essential for a comprehensive understanding of theseastrophysical objects. We introduce a novel, efficient method for modeling theSEDs of blazars by the mean of a convolutional neural network (CNN). In thispaper, we trained the CNN on a leptonic model that incorporates synchrotron andinverse Compton emissions, as well as self-consistent electron cooling and paircreation-annihilation processes. The CNN is capable of reproducing theradiative signatures of blazars with high accuracy. This approach significantlyreduces computational time, thereby enabling real-time fitting tomulti-wavelength datasets. As a demonstration, we used the trained CNN withMultiNest to fit the broadband SEDs of Mrk 421 and 1ES 1959+650, successfullyobtaining their parameter posterior distributions. This novel framework forfitting the SEDs of blazars will be further extended to incorporate moresophisticated models based on external Compton and hadronic scenarios, allowingfor multi-messenger constraints in the analysis. The models will be madepublicly available via a web interface, the Markarian MultiwavelengthDatacenter, to facilitate self-consistent modeling of multi-messenger data fromblazar observations.

Modeling blazar broadband emission with convolutional neural networks -- I. Synchrotron self-Compton model